Page 190 - IJB-10-2

International

Journal of Bioprinting

REVIEW ARTICLE
Optimizing cell deposition for inkjet-based

bioprinting

Wei Long Ng *, Viktor Shkolnikov *
2
1
1 Singapore Centre for 3D Printing (SC3DP), School of Mechanical and Aerospace Engineering,
Nanyang Technological University, Singapore
2 HP Inc., 1501 Page Mill Road, Palo Alto, California, United States of America
(This article belongs to the Special Issue: Special Issue of International Journal of Bioprinting in the BDMC
2023 Conference)

Abstract
Although inkjet-based bioprinting enables precise drop-on-demand cell deposition
within three-dimensional (3D) tissue constructs and facilitates critical cell–cell and
cell–matrix interactions, it faces challenges such as poor cell homogeneity and low
cell viability. To date, there is a lack of comprehensive review papers addressing the
optimization of cell deposition in inkjet-based bioprinting. This review aims to fill
that gap by providing an overview of various critical aspects in bioprinting, ranging
from bio-ink properties to the impact of printed droplets. The bio-ink section begins
by exploring how cells influence the physical properties of bio-inks and emphasizes
the significance of achieving cell homogeneity within bio-inks to ensure consistent
and reliable printing. The discussion then delves into inkjet-based printing chambers
(thermal and piezoelectric), the effect of shear stress on printed cells, droplet
formation dynamics, the influence of polymer-based and cell-laden droplets on the
*Corresponding authors: underlying substrate surface, and the dynamics of droplet impact. Beyond droplet
Wei Long Ng
(ng.wl@ntu.edu.sg) formation and impact, the review highlights the importance of biophysical and
Viktor Shkolnikov biological cues within 3D hydrogel matrices for cell proliferation and differentiation.
(viktors@hp.com) Finally, the paper highlights current and potential applications, with a specific focus
Citation: Ng WL, Shkolnikov V. on skin and lung tissue engineering using inkjet-based bioprinting techniques, and
Optimizing cell deposition for provides insights into the emerging role of machine learning in optimizing the cell
inkjet-based bioprinting. Int J deposition process for inkjet-based bioprinting.
Bioprint. 2024;10(2):2135.
doi: 10.36922/ijb.2135
Received: October 29, 2023 Keywords: 3D bioprinting; Biofabrication; Inkjet bioprinting; Cells; Bio-inks;
Accepted: January 2, 2024 Machine learning
Published Online: February 5, 2024
Copyright: © 2024 Author(s).
This is an Open Access article
distributed under the terms of the
Creative Commons Attribution 1. Introduction
License, permitting distribution,
and reproduction in any medium, The field of bioprinting has sparked a profound and ever-growing interest due to its
provided the original work is potential to fabricate complex three-dimensional (3D) cell-laden structures in a highly
properly cited. repeatable and scalable manner. The convergence of engineering, materials science,
1-3
Publisher’s Note: AccScience and biology has ushered into a new era of possibilities, where fabrication of functional
Publishing remains neutral with 3D tissue constructs becomes a reality. 3D bioprinting techniques can be categorized
regard to jurisdictional claims in 4-8 9-14
published maps and institutional into three distinct classifications, namely jetting-based, extrusion-based, and vat
affiliations. photopolymerization-based bioprinting. 15,16 Each bioprinting technique has its specific

Volume 10 Issue 2 (2024) 182 doi: 10.36922/ijb.2135